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Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.

Feng S, Du YQ, Zhang L, Zhang L, Feng RR, Liu SY - Chin. Med. J. (2015)

Bottom Line: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups.The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms.The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Laboratory, Haihe Hospital, Respiratory Disease Research Institute, Tianjin 300350, China.

ABSTRACT

Background: Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.

Methods: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.

Results: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.

Conclusion: The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

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Related in: MedlinePlus

Principal component analysis (PCA) model. PCA scores plots of the SIMCA-P + 12.0.1.0 generated data, showing tuberculosis (TB) patients versus healthy controls and the non-TB group collected serum samples before the removal of ‘noise’ and interfering compounds from the dataset. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.
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Figure 3: Principal component analysis (PCA) model. PCA scores plots of the SIMCA-P + 12.0.1.0 generated data, showing tuberculosis (TB) patients versus healthy controls and the non-TB group collected serum samples before the removal of ‘noise’ and interfering compounds from the dataset. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.

Mentions: As mentioned above, the overall peak profiles of the three groups looked quite different, which suggested that these profiles could be used to discriminate TB from the controls. For the holistic treatment of these data, multivariate analysis was used to identify the metabolomic differences between the groups. For data reduction and pattern recognition (PR), a series of PR methods were applied using SIMCA-P 12.1 software (Umetrics, Sweden). Principal component analysis (PCA) was initially applied to the data to visualize inherent clustering between the three groups. PCA involves the transformation of a multidimensional set of possibly correlated variables into two linearly uncorrelated dimensions. This model explained an estimated 41.4% of the original data (R2 = 0.414 and Q2 = 0.334) [Figure 3]. However, in addition to the effects of the diseases on the metabolome, there were other factors that are known to contribute to differences of endogenous metabolites, such as age and diet.


Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.

Feng S, Du YQ, Zhang L, Zhang L, Feng RR, Liu SY - Chin. Med. J. (2015)

Principal component analysis (PCA) model. PCA scores plots of the SIMCA-P + 12.0.1.0 generated data, showing tuberculosis (TB) patients versus healthy controls and the non-TB group collected serum samples before the removal of ‘noise’ and interfering compounds from the dataset. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837832&req=5

Figure 3: Principal component analysis (PCA) model. PCA scores plots of the SIMCA-P + 12.0.1.0 generated data, showing tuberculosis (TB) patients versus healthy controls and the non-TB group collected serum samples before the removal of ‘noise’ and interfering compounds from the dataset. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.
Mentions: As mentioned above, the overall peak profiles of the three groups looked quite different, which suggested that these profiles could be used to discriminate TB from the controls. For the holistic treatment of these data, multivariate analysis was used to identify the metabolomic differences between the groups. For data reduction and pattern recognition (PR), a series of PR methods were applied using SIMCA-P 12.1 software (Umetrics, Sweden). Principal component analysis (PCA) was initially applied to the data to visualize inherent clustering between the three groups. PCA involves the transformation of a multidimensional set of possibly correlated variables into two linearly uncorrelated dimensions. This model explained an estimated 41.4% of the original data (R2 = 0.414 and Q2 = 0.334) [Figure 3]. However, in addition to the effects of the diseases on the metabolome, there were other factors that are known to contribute to differences of endogenous metabolites, such as age and diet.

Bottom Line: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups.The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms.The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Laboratory, Haihe Hospital, Respiratory Disease Research Institute, Tianjin 300350, China.

ABSTRACT

Background: Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.

Methods: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.

Results: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.

Conclusion: The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Show MeSH
Related in: MedlinePlus